Commit From AutoNLP

.gitattributes

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 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zstandard filter=lfs diff=lfs merge=lfs -text
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+*.tar.gz filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+tags: autonlp
+language: unk
+widget:
+- text: "I love AutoNLP 🤗"
+datasets:
+- rodrigogelacio/autonlp-data-department-classification
+co2_eq_emissions: 1.4862856774320061
+---
+
+# Model Trained Using AutoNLP
+
+- Problem type: Multi-class Classification
+- Model ID: 534915130
+- CO2 Emissions (in grams): 1.4862856774320061
+
+## Validation Metrics
+
+- Loss: 0.37066277861595154
+- Accuracy: 0.9204545454545454
+- Macro F1: 0.9103715740678612
+- Micro F1: 0.9204545454545455
+- Weighted F1: 0.9196871607509906
+- Macro Precision: 0.9207759152612094
+- Micro Precision: 0.9204545454545454
+- Weighted Precision: 0.922177301864802
+- Macro Recall: 0.9055002187355129
+- Micro Recall: 0.9204545454545454
+- Weighted Recall: 0.9204545454545454
+
+
+## Usage
+
+You can use cURL to access this model:
+
+```
+$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoNLP"}' https://api-inference.huggingface.co/models/rodrigogelacio/autonlp-department-classification-534915130
+```
+
+Or Python API:
+
+```
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+
+model = AutoModelForSequenceClassification.from_pretrained("rodrigogelacio/autonlp-department-classification-534915130", use_auth_token=True)
+
+tokenizer = AutoTokenizer.from_pretrained("rodrigogelacio/autonlp-department-classification-534915130", use_auth_token=True)
+
+inputs = tokenizer("I love AutoNLP", return_tensors="pt")
+
+outputs = model(**inputs)
+```

config.json

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+{
+  "_name_or_path": "AutoNLP",
+  "_num_labels": 12,
+  "architectures": [
+    "BertForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "asta",
+    "1": "bim",
+    "2": "fachschaften",
+    "3": "familienbuero",
+    "4": "frauen_und_gleichstellung",
+    "5": "international_office",
+    "6": "lanadema",
+    "7": "mathecafe",
+    "8": "pruefungsamt",
+    "9": "studienverlaufsberatung",
+    "10": "studieren_mit_behinderung",
+    "11": "studierendensekretariat"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "asta": 0,
+    "bim": 1,
+    "fachschaften": 2,
+    "familienbuero": 3,
+    "frauen_und_gleichstellung": 4,
+    "international_office": 5,
+    "lanadema": 6,
+    "mathecafe": 7,
+    "pruefungsamt": 8,
+    "studienverlaufsberatung": 9,
+    "studieren_mit_behinderung": 10,
+    "studierendensekretariat": 11
+  },
+  "layer_norm_eps": 1e-12,
+  "max_length": 64,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "padding": "max_length",
+  "position_embedding_type": "absolute",
+  "problem_type": "single_label_classification",
+  "torch_dtype": "float32",
+  "transformers_version": "4.15.0",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30000
+}

pytorch_model.bin

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sample_input.pkl

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special_tokens_map.json

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+{"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"}

tokenizer_config.json

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+{"do_lower_case": false, "unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]", "tokenize_chinese_chars": true, "strip_accents": null, "model_max_length": 512, "special_tokens_map_file": null, "name_or_path": "AutoNLP", "tokenizer_class": "BertTokenizer"}